| Zap Q-learning AM Devraj, S Meyn Advances in Neural Information Processing Systems 30, 2017 | 105 | 2017 |
| Fastest convergence for Q-learning AM Devraj, SP Meyn arXiv preprint arXiv:1707.03770, 2017 | 48 | 2017 |
| Reinforcement learning for control of building HVAC systems NS Raman, AM Devraj, P Barooah, SP Meyn 2020 American Control Conference (ACC), 2326-2332, 2020 | 40 | 2020 |
| Explicit mean-square error bounds for monte-carlo and linear stochastic approximation S Chen, A Devraj, A Busic, S Meyn International Conference on Artificial Intelligence and Statistics, 4173-4183, 2020 | 40 | 2020 |
| Model-free primal-dual methods for network optimization with application to real-time optimal power flow Y Chen, A Bernstein, A Devraj, S Meyn 2020 American Control Conference (ACC), 3140-3147, 2020 | 32 | 2020 |
| Fundamental design principles for reinforcement learning algorithms AM Devraj, A Bušić, S Meyn Handbook of Reinforcement Learning and Control, 75-137, 2021 | 22 | 2021 |
| The ODE method for asymptotic statistics in stochastic approximation and reinforcement learning V Borkar, S Chen, A Devraj, I Kontoyiannis, S Meyn arXiv preprint arXiv:2110.14427, 2021 | 19 | 2021 |
| Zap Q-Learning with nonlinear function approximation S Chen, AM Devraj, F Lu, A Busic, S Meyn Advances in Neural Information Processing Systems 33, 16879-16890, 2020 | 19 | 2020 |
| Learning techniques for feedback particle filter design A Radhakrishnan, A Devraj, S Meyn 2016 IEEE 55th Conference on Decision and Control (CDC), 5453-5459, 2016 | 18 | 2016 |
| Differential TD learning for value function approximation AM Devraj, SP Meyn Decision and Control (CDC), 2016 IEEE 55th Conference on, 6347-6354, 2016 | 17 | 2016 |
| Zap Q-Learning-a user's guide AM Devraj, A Bušić, S Meyn 2019 Fifth Indian Control Conference (ICC), 10-15, 2019 | 16 | 2019 |
| On matrix momentum stochastic approximation and applications to Q-learning AM Devraj, A Bušić, S Meyn 2019 57th Annual Allerton Conference on Communication, Control, and …, 2019 | 15* | 2019 |
| Q-learning with uniformly bounded variance AM Devraj, SP Meyn IEEE Transactions on Automatic Control 67 (11), 5948-5963, 2021 | 14 | 2021 |
| Stochastic variance reduced primal dual algorithms for empirical composition optimization AM Devraj, J Chen Advances in Neural Information Processing Systems 32, 2019 | 14 | 2019 |
| Q-learning with uniformly bounded variance: Large discounting is not a barrier to fast learning AM Devraj, SP Meyn arXiv preprint arXiv:2002.10301, 2020 | 13 | 2020 |
| Differential temporal difference learning AM Devraj, I Kontoyiannis, SP Meyn IEEE Transactions on Automatic Control 66 (10), 4652-4667, 2020 | 12 | 2020 |
| Accelerating optimization and reinforcement learning with quasi stochastic approximation S Chen, A Devraj, A Bernstein, S Meyn 2021 American Control Conference (ACC), 1965-1972, 2021 | 11 | 2021 |
| Zap Q-Learning for optimal stopping S Chen, AM Devraj, A Bušić, S Meyn 2020 American Control Conference (ACC), 3920-3925, 2020 | 11 | 2020 |
| Power allocation in energy harvesting sensors with ARQ: A convex optimization approach AM Devraj, MK Sharma, CR Murthy 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP …, 2014 | 10 | 2014 |
| Zap q-learning with nonlinear function approximation S Chen, AM Devraj, F Lu, A Bušić, SP Meyn arXiv preprint arXiv:1910.05405, 2019 | 9 | 2019 |
Sean MeynProfessor of ECE and Robert C. Pittman Eminent Scholar ChairVerified email at ece.ufl.edu
